Description
Usage
Arguments
Details
Value
View source: R/eifs.R
Iterative IPCW Update Procedure of Efficient Influence Function
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14  (
data_in,
,
V,
ipc_mech,
ipc_weights,
ipc_weights_norm,
Qn_estim,
Hn_estim,
estimator = ("tmle", "onestep"),
fluctuation = ,
flucmod_tol = 100,
eif_reg_type = ("hal", "glm")
)

data_in 
A data.table containing variables and observations of
full data. That is, this corresponds to the data after application of a
censoring process.

C 
A numeric binary vector giving the censoring status of a
given observation.

V 
A data.table giving the values across all observations of
all variables that play a role in the censoring mechanism.

ipc_mech 
A numeric vector containing values that describe the
censoring mechanism for all of the observations. Note that such values are
estimated by regressing the censoring covariates V on the observed
censoring C and thus correspond to predicted probabilities of being
censored for each observation.

ipc_weights 
A numeric vector of inverse probability of
censoring weights. These are equivalent to C / ipc_mech in any
initial run of this function. Updated values of this vector are provided as
part of the output of this function, which may be used in subsequent calls
that allow convergence to a more efficient estimate.

ipc_weights_norm 
A numeric vector of the weights described in
the previous argument. In this case, the weights are normalized.

Qn_estim 
A data.table corresponding to the outcome regression.
This is produced by invoking the internal function est_Q .

Hn_estim 
A data.table corresponding to values produced in the
computation of the auxiliary ("clever") covariate. This is produced easily
by invoking the internal function est_Hn .

estimator 
The type of estimator to be fit, either "tmle" for
targeted maximum likelihood estimation or "onestep" for a onestep
estimator.

fluctuation 
A character giving the type of regression to be
used in traversing the fluctuation submodel. The choices are "weighted" and
"standard".

flucmod_tol 
A numeric indicating the largest value to be
tolerated in the fluctuation model for the targeted minimum loss estimator.

eif_reg_type 
Whether a flexible nonparametric function ought to be
used in the dimensionreduced nuisance regression of the targeting step for
the censored data case. By default, the method used is a nonparametric
regression based on the Highly Adaptive Lasso (from hal9001). Set
this to "glm" to instead use a simple linear regression model. In
this step, the efficient influence function (EIF) is regressed against
covariates contributing to the censoring mechanism (i.e., EIF ~ V  C = 1).

An adaptation of the IPCWTMLE for iteratively constructing an
efficient inverse probability of censoring weighted TML or onestep
estimator. The efficient influence function of the parameter and updating
the IPC weights in an iterative process, until a convergence criteria is
satisfied.
A list
containing the estimated outcome mechanism, the fitted
fluctuation model for TML updates, the updated inverse probability of
censoring weights (IPCW), normalized versions of the same weights, the
updated estimate of the efficient influence function, and the estimated
IPCW component of the EIF.
txshift documentation built on Oct. 23, 2020, 8:27 p.m.